Geometric Morphometrics
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(Momocs)##
## Attaching package: 'Momocs'## The following objects are masked from 'package:dplyr':
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## arrange, combine, filter, mutate, rename, sample_frac, sample_n,
## select, slice## The following object is masked from 'package:tidyr':
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## chop## The following object is masked from 'package:stats':
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## filterlibrary(geomorph)## Loading required package: RRPP## Loading required package: rgl## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
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## expand, pack, unpack##
## Attaching package: 'geomorph'## The following object is masked from 'package:Momocs':
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## mosquitolibrary(here)## here() starts at F:/Google Drive/Lectures/2021 BYU Digital ArchaeologyOutline analysis
This code shows how outline analysis can be conducted
ls = list.files(here("JPGs"),
pattern = "jpg",
full.names = T)
img = import_jpg(ls)## Extracting 152.jpg outlines...## [ 1 / 152 ] Chaco_01.jpg
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## [ 152 / 152 ] PSN_47.jpg## Done in 1.6 secsout = img %>%
Out %>%
Momocs::coo_smooth(100) %>%
coo_center()## Warning: `data_frame()` was deprecated in tibble 1.1.0.
## Please use `tibble()` instead.out %>% panel
fac = tibble(file = ls,name = names(out)) %>%
separate(name,
into = c("type","no"),
sep = "_",
remove = F) %>%
mutate_at(vars(type),factor)## Warning: Expected 2 pieces. Additional pieces discarded in 33 rows [28, 29, 30,
## 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, ...].out$fac = fac
out %>% panel(fac = "type", palette = col_gallus)
c = calibrate_harmonicpower_efourier(out,nb.h = 25)
# use 99% result
nh = c$minh[3]
outEFA = out %>%
efourier(nh)## `norm=TRUE` is used and this may be troublesome. See ?efourieroutPCA = outEFA %>% PCA
outPCA %>% plot_PCA("type")
outPCA %>% LDA("type") %>% plot_LDA()## 18 PC retained
outPCA = out %>%
coo_sample(100) %>%
coo_align() %>%
coo_rotate(1.5708) %>%
Ldk() %>%
fgProcrustes() %>%
PCA## iteration: 1 gain: 2545300
## iteration: 2 gain: 1.1228
## iteration: 3 gain: 0.49334
## iteration: 4 gain: 0.015884
## iteration: 5 gain: 0.3369
## iteration: 6 gain: 0.16997
## iteration: 7 gain: 0.016626
## iteration: 8 gain: 0.0010921
## iteration: 9 gain: 0.0013964
## iteration: 10 gain: 0.012359
## iteration: 11 gain: 0.011032
## iteration: 12 gain: 0.0004922
## iteration: 13 gain: 0.0056553
## iteration: 14 gain: 0.0051922
## iteration: 15 gain: 0.0024029
## iteration: 16 gain: 9.0429e-05
## iteration: 17 gain: 0.0010722
## iteration: 18 gain: 0.0011716
## iteration: 19 gain: 0.0006391
## iteration: 20 gain: 3.546e-05
## iteration: 21 gain: 0.00028887
## iteration: 22 gain: 0.00030528
## iteration: 23 gain: 0.00016177
## iteration: 24 gain: 1.0428e-05
## iteration: 25 gain: 7.059e-05
## iteration: 26 gain: 7.6441e-05
## iteration: 27 gain: 4.1415e-05
## iteration: 28 gain: 3.1573e-06
## iteration: 29 gain: 1.7659e-05
## iteration: 30 gain: 1.9338e-05
## iteration: 31 gain: 1.0575e-05
## iteration: 32 gain: 9.2186e-07
## iteration: 33 gain: 4.3869e-06
## iteration: 34 gain: 4.8773e-06
## iteration: 35 gain: 2.7015e-06
## iteration: 36 gain: 2.658e-07
## iteration: 37 gain: 1.0906e-06
## iteration: 38 gain: 1.2307e-06
## iteration: 39 gain: 6.9001e-07
## iteration: 40 gain: 7.5587e-08
## iteration: 41 gain: 2.7078e-07
## iteration: 42 gain: 3.1043e-07
## iteration: 43 gain: 1.7623e-07
## iteration: 44 gain: 2.1277e-08
## iteration: 45 gain: 6.7171e-08
## iteration: 46 gain: 7.828e-08
## iteration: 47 gain: 4.5004e-08
## iteration: 48 gain: 5.9376e-09
## iteration: 49 gain: 1.6646e-08
## iteration: 50 gain: 1.9734e-08
## iteration: 51 gain: 1.1491e-08
## iteration: 52 gain: 1.6453e-09
## iteration: 53 gain: 4.1205e-09
## iteration: 54 gain: 4.9731e-09
## iteration: 55 gain: 2.9336e-09
## iteration: 56 gain: 4.5293e-10
## iteration: 57 gain: 1.0186e-09
## iteration: 58 gain: 1.2528e-09
## iteration: 59 gain: 7.4897e-10
## iteration: 60 gain: 1.2415e-10
## iteration: 61 gain: 2.5193e-10
## iteration: 62 gain: 3.1559e-10
## iteration: 63 gain: 1.9099e-10
## iteration: 64 gain: 3.3651e-11outPCA %>% plot_PCA("type")
outPCA %>% LDA("type") %>% plot_LDA()## 25 PC retained
First step for 3D gm is to digitize the landmarks
The geomorph package can be used for this. This vignette shows us how to do it.
data("scallopPLY")
my.ply <- scallopPLY$ply
fixed.lms1 <- digit.fixed(spec = my.ply, fixed = 5)
my.ply.2 <- scallopPLY$ply
fixed.lms2 <- digit.fixed(my.ply.2, 5)
surf.pts1 <- buildtemplate(spec = my.ply,
fixed = fixed.lms1,
surface.sliders = 100)
surf.pts2 <- digitsurface(spec = my.ply.2,
fixed = fixed.lms2)The above code didn’t work when I tried it on projectile points.
I got an automatic process to work with Artifact GeoMorph Toolbox 3D
library(rio)##
## Attaching package: 'rio'## The following object is masked from 'package:Momocs':
##
## exportls = list.files(here("COADSDataset-Ohio/"),
pattern = "xlsx",
full.names = T)
nms = list.files(here("COADSDataset-Ohio/"),
pattern = "xlsx",
full.names = F) %>%
gsub(".xlsx","",.)
lndmrks = map_dfr(ls,~{
tmp = import(.x)
}) %>% as.matrix %>%
arrayspecs(800,3)
gpa = gpagen(lndmrks)##
## Performing GPA
##
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|================== | 25%
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##
## Making projections... Finished!summary(gpa)##
## Call:
## gpagen(A = lndmrks)
##
##
##
## Generalized Procrustes Analysis
## with Partial Procrustes Superimposition
##
## 800 fixed landmarks
## 0 semilandmarks (sliders)
## 3-dimensional landmarks
## 2 GPA iterations to converge
##
##
## Consensus (mean) Configuration
##
## X Y Z
## 1 -0.052667917 5.266810e-03 1.061008e-04
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## 739 0.046712610 1.595769e-02 1.814288e-03
## 740 0.046701311 2.044212e-02 -1.622515e-04
## 741 0.049537157 1.905339e-02 -1.263110e-03
## 742 0.049551383 1.493814e-02 -2.842337e-03
## 743 0.049564355 1.082126e-02 -3.451185e-03
## 744 0.049577441 6.699460e-03 -3.336332e-03
## 745 0.049589983 2.576340e-03 -3.314363e-03
## 746 0.049602302 -1.547209e-03 -3.256087e-03
## 747 0.049615651 -5.669678e-03 -2.971098e-03
## 748 0.049628886 -9.795451e-03 -2.496942e-03
## 749 0.049641328 -1.392448e-02 -1.755267e-03
## 750 0.049653328 -1.805779e-02 -6.958285e-05
## 751 0.049652882 -1.806322e-02 1.130405e-03
## 752 0.049636350 -1.395646e-02 3.285764e-03
## 753 0.049622011 -9.835219e-03 3.466920e-03
## 754 0.049606940 -5.711660e-03 3.354989e-03
## 755 0.049593565 -1.587955e-03 3.234211e-03
## 756 0.049580757 2.537872e-03 2.877167e-03
## 757 0.049568627 6.661690e-03 2.551146e-03
## 758 0.049557495 1.078495e-02 2.249271e-03
## 759 0.049546520 1.491012e-02 1.565047e-03
## 760 0.049535783 1.904838e-02 -4.244173e-04
## 761 0.052374817 1.591788e-02 -9.230404e-04
## 762 0.052385671 1.263743e-02 -2.178854e-03
## 763 0.052409049 8.374854e-03 -2.260147e-03
## 764 0.052419484 5.070302e-03 -2.217362e-03
## 765 0.052430608 1.777863e-03 -2.138220e-03
## 766 0.052440810 -1.511372e-03 -2.178896e-03
## 767 0.052452703 -4.802467e-03 -1.984901e-03
## 768 0.052464497 -8.095186e-03 -1.534018e-03
## 769 0.052474945 -1.139010e-02 -1.030532e-03
## 770 0.052483646 -1.468928e-02 6.841018e-05
## 771 0.052482596 -1.469680e-02 1.187711e-03
## 772 0.052471505 -1.141454e-02 2.587176e-03
## 773 0.052459421 -8.123980e-03 2.784289e-03
## 774 0.052446607 -4.831130e-03 2.533453e-03
## 775 0.052435702 -1.538463e-03 2.289717e-03
## 776 0.052424428 1.754547e-03 1.915297e-03
## 777 0.052413891 5.047830e-03 1.528819e-03
## 778 0.052403127 8.350974e-03 1.459998e-03
## 779 0.052381569 1.261605e-02 1.224388e-03
## 780 0.052373449 1.591213e-02 1.398613e-04
## 781 0.052537366 1.572031e-02 -9.752965e-04
## 782 0.053785456 1.250415e-02 -9.638451e-04
## 783 0.054212459 9.218357e-03 -7.285493e-04
## 784 0.054428565 4.671589e-03 -7.294579e-04
## 785 0.054600235 1.369621e-03 -3.459809e-04
## 786 0.054803385 -1.949813e-03 -2.133717e-04
## 787 0.054817612 -5.176802e-03 -5.381431e-05
## 788 0.054541350 -8.520142e-03 1.527212e-04
## 789 0.054137229 -1.159491e-02 2.320453e-04
## 790 0.052575182 -1.458291e-02 2.013420e-04
## 791 0.052574594 -1.458900e-02 1.142589e-03
## 792 0.054135569 -1.160053e-02 1.280171e-03
## 793 0.054540304 -8.526785e-03 1.329660e-03
## 794 0.054816932 -5.181950e-03 8.601824e-04
## 795 0.054802671 -1.955406e-03 4.493209e-04
## 796 0.054599639 1.364968e-03 2.522633e-04
## 797 0.054428038 4.666895e-03 6.707766e-06
## 798 0.054211914 9.212238e-03 1.552242e-04
## 799 0.053784712 1.249729e-02 -7.505829e-05
## 800 0.052535431 1.571357e-02 2.986010e-04# plotAllSpecimens(gpa$coords)
pca = gm.prcomp(gpa$coords)
plotdf = pca$x[,1:2] %>%
as_tibble() %>%
mutate(names = nms)
plotdf %>% ggplot(aes(Comp1,Comp2,label = names)) +
geom_point()
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:rio':
##
## export## The following objects are masked from 'package:Momocs':
##
## arrange, export, filter, mutate, rename, select, slice## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly()